Beginner Explanation
Imagine you have a big box of toys (that’s your visual data) and you want to tell your friend what toys you have without showing them the whole box. Instead, you write a short note (that’s the instruction token) describing the toys. TransV is like that note; it takes a lot of visual information and condenses it into a simpler form that still tells your friend what they need to know. This way, your friend can understand what toys you have without needing to see every single one!Technical Explanation
TransV is a token information transfer module designed for multimodal models, which allows the compression of visual tokens into instruction tokens. It utilizes techniques such as attention mechanisms to ensure that important features from the visual input are preserved during the transformation. Below is a simplified example using PyTorch: “`python import torch import torch.nn as nn class TransV(nn.Module): def __init__(self, input_dim, output_dim): super(TransV, self).__init__() self.fc = nn.Linear(input_dim, output_dim) def forward(self, visual_tokens): return self.fc(visual_tokens) # Example usage visual_tokens = torch.randn(10, 256) # 10 tokens, each of 256 dimensions transv = TransV(256, 128) # Compress to 128 dimensions instruction_tokens = transv(visual_tokens) “` This model learns to compress the visual tokens while retaining essential information, allowing for effective instruction generation from visual data.Academic Context
TransV operates at the intersection of computer vision and natural language processing, drawing from foundational theories in multimodal learning. It builds upon concepts such as attention mechanisms (Vaswani et al., 2017) and tokenization strategies that facilitate the integration of visual and textual data. The mathematical underpinning involves linear transformations and feature extraction that are critical for maintaining the integrity of the information during the compression process. Key papers include ‘Attention is All You Need’ and recent advancements in multimodal transformers that explore the interplay of vision and language.Code Examples
Example 1:
import torch
import torch.nn as nn
class TransV(nn.Module):
def __init__(self, input_dim, output_dim):
super(TransV, self).__init__()
self.fc = nn.Linear(input_dim, output_dim)
def forward(self, visual_tokens):
return self.fc(visual_tokens)
# Example usage
visual_tokens = torch.randn(10, 256) # 10 tokens, each of 256 dimensions
transv = TransV(256, 128) # Compress to 128 dimensions
instruction_tokens = transv(visual_tokens)Example 2:
def __init__(self, input_dim, output_dim):
super(TransV, self).__init__()
self.fc = nn.Linear(input_dim, output_dim)Example 3:
def forward(self, visual_tokens):
return self.fc(visual_tokens)Example 4:
import torch
import torch.nn as nn
class TransV(nn.Module):
def __init__(self, input_dim, output_dim):Example 5:
import torch.nn as nn
class TransV(nn.Module):
def __init__(self, input_dim, output_dim):
super(TransV, self).__init__()Example 6:
class TransV(nn.Module):
def __init__(self, input_dim, output_dim):
super(TransV, self).__init__()
self.fc = nn.Linear(input_dim, output_dim)
Example 7:
def __init__(self, input_dim, output_dim):
super(TransV, self).__init__()
self.fc = nn.Linear(input_dim, output_dim)
def forward(self, visual_tokens):Example 8:
def forward(self, visual_tokens):
return self.fc(visual_tokens)
# Example usage
visual_tokens = torch.randn(10, 256) # 10 tokens, each of 256 dimensionsView Source: https://arxiv.org/abs/2511.16595v1